Overview

Dataset statistics

Number of variables23
Number of observations85331
Missing cells1003193
Missing cells (%)51.1%
Duplicate rows391
Duplicate rows (%)0.5%
Total size in memory15.0 MiB
Average record size in memory184.0 B

Variable types

Text3
Unsupported1
Categorical4
Numeric15

Alerts

Year has constant value ""Constant
Dataset has 391 (0.5%) duplicate rowsDuplicates
Back Squat (lbs) is highly overall correlated with Clean and Jerk (lbs) and 5 other fieldsHigh correlation
Clean and Jerk (lbs) is highly overall correlated with Back Squat (lbs) and 8 other fieldsHigh correlation
Deadlift (lbs) is highly overall correlated with Back Squat (lbs) and 4 other fieldsHigh correlation
Division is highly overall correlated with Snatch (lbs)High correlation
Fight Gone Bad is highly overall correlated with Clean and Jerk (lbs) and 9 other fieldsHigh correlation
Filthy 50 (s) is highly overall correlated with Fight Gone Bad and 4 other fieldsHigh correlation
Fran (s) is highly overall correlated with Back Squat (lbs) and 9 other fieldsHigh correlation
Grace (s) is highly overall correlated with Back Squat (lbs) and 6 other fieldsHigh correlation
Helen (s) is highly overall correlated with Clean and Jerk (lbs) and 9 other fieldsHigh correlation
L1 Benchmark (s) is highly overall correlated with Fight Gone Bad and 5 other fieldsHigh correlation
Max Pull-ups is highly overall correlated with Back Squat (lbs) and 9 other fieldsHigh correlation
Qualifier is highly overall correlated with RankHigh correlation
Rank is highly overall correlated with QualifierHigh correlation
Run 5k (s) is highly overall correlated with Fight Gone Bad and 2 other fieldsHigh correlation
Snatch (lbs) is highly overall correlated with Back Squat (lbs) and 8 other fieldsHigh correlation
Sprint 400m (s) is highly overall correlated with Clean and Jerk (lbs) and 4 other fieldsHigh correlation
Division is highly imbalanced (51.9%)Imbalance
Games_Level is highly imbalanced (80.4%)Imbalance
Qualifier is highly imbalanced (57.5%)Imbalance
Affiliate has 25090 (29.4%) missing valuesMissing
Country has 85331 (100.0%) missing valuesMissing
Region has 2474 (2.9%) missing valuesMissing
Division has 2474 (2.9%) missing valuesMissing
Rank has 57254 (67.1%) missing valuesMissing
Games_Level has 57254 (67.1%) missing valuesMissing
Year has 57254 (67.1%) missing valuesMissing
Qualifier has 57254 (67.1%) missing valuesMissing
Back Squat (lbs) has 7460 (8.7%) missing valuesMissing
Clean and Jerk (lbs) has 10332 (12.1%) missing valuesMissing
Deadlift (lbs) has 5954 (7.0%) missing valuesMissing
Snatch (lbs) has 13051 (15.3%) missing valuesMissing
Fight Gone Bad has 63744 (74.7%) missing valuesMissing
Max Pull-ups has 49677 (58.2%) missing valuesMissing
Chad1000x (s) has 84791 (99.4%) missing valuesMissing
L1 Benchmark (s) has 85182 (99.8%) missing valuesMissing
Filthy 50 (s) has 70433 (82.5%) missing valuesMissing
Fran (s) has 39621 (46.4%) missing valuesMissing
Grace (s) has 48249 (56.5%) missing valuesMissing
Helen (s) has 58791 (68.9%) missing valuesMissing
Run 5k (s) has 54881 (64.3%) missing valuesMissing
Sprint 400m (s) has 66642 (78.1%) missing valuesMissing
Country is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-02-17 03:19:11.266802
Analysis finished2024-02-17 03:19:39.687731
Duration28.42 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Distinct81868
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size666.8 KiB
2024-02-16T22:19:39.900786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length45
Median length36
Mean length13.527581
Min length3

Characters and Unicode

Total characters1154322
Distinct characters194
Distinct categories16 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79211 ?
Unique (%)92.8%

Sample

1st rowYagiz Diyaroglu
2nd rowJandré Erasmus
3rd rowRoan van Heerden
4th rowDavid Swarts
5th rowColin Pinkham
ValueCountFrequency (%)
michael 1251
 
0.7%
jason 1082
 
0.6%
david 1043
 
0.6%
brian 830
 
0.5%
smith 815
 
0.5%
john 784
 
0.4%
ryan 719
 
0.4%
chris 711
 
0.4%
daniel 707
 
0.4%
andrew 681
 
0.4%
Other values (47206) 167013
95.1%
2024-02-16T22:19:40.313940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 109593
 
9.5%
e 106955
 
9.3%
90318
 
7.8%
n 83040
 
7.2%
r 77596
 
6.7%
i 74313
 
6.4%
o 64638
 
5.6%
l 56348
 
4.9%
s 46419
 
4.0%
t 41640
 
3.6%
Other values (184) 403462
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 883401
76.5%
Uppercase Letter 178735
 
15.5%
Space Separator 90318
 
7.8%
Dash Punctuation 1158
 
0.1%
Other Punctuation 662
 
0.1%
Other Letter 13
 
< 0.1%
Final Punctuation 9
 
< 0.1%
Decimal Number 7
 
< 0.1%
Open Punctuation 6
 
< 0.1%
Close Punctuation 6
 
< 0.1%
Other values (6) 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 109593
12.4%
e 106955
12.1%
n 83040
9.4%
r 77596
8.8%
i 74313
 
8.4%
o 64638
 
7.3%
l 56348
 
6.4%
s 46419
 
5.3%
t 41640
 
4.7%
h 31198
 
3.5%
Other values (85) 191661
21.7%
Uppercase Letter
ValueCountFrequency (%)
M 17464
 
9.8%
J 14667
 
8.2%
S 14361
 
8.0%
C 13775
 
7.7%
B 12982
 
7.3%
A 12848
 
7.2%
R 9829
 
5.5%
D 9766
 
5.5%
L 8677
 
4.9%
K 8126
 
4.5%
Other values (56) 56240
31.5%
Other Letter
ValueCountFrequency (%)
2
15.4%
2
15.4%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
Decimal Number
ValueCountFrequency (%)
1 2
28.6%
7 1
14.3%
4 1
14.3%
3 1
14.3%
9 1
14.3%
0 1
14.3%
Other Punctuation
ValueCountFrequency (%)
' 376
56.8%
. 283
42.7%
, 3
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 1157
99.9%
1
 
0.1%
Final Punctuation
ValueCountFrequency (%)
8
88.9%
1
 
11.1%
Space Separator
ValueCountFrequency (%)
90318
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%
Other Number
ValueCountFrequency (%)
³ 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Other Symbol
ValueCountFrequency (%)
¦ 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1062046
92.0%
Common 92173
 
8.0%
Cyrillic 73
 
< 0.1%
Greek 17
 
< 0.1%
Hangul 13
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 109593
 
10.3%
e 106955
 
10.1%
n 83040
 
7.8%
r 77596
 
7.3%
i 74313
 
7.0%
o 64638
 
6.1%
l 56348
 
5.3%
s 46419
 
4.4%
t 41640
 
3.9%
h 31198
 
2.9%
Other values (112) 370306
34.9%
Cyrillic
ValueCountFrequency (%)
и 8
 
11.0%
о 7
 
9.6%
н 5
 
6.8%
т 5
 
6.8%
р 4
 
5.5%
в 4
 
5.5%
с 4
 
5.5%
а 4
 
5.5%
й 3
 
4.1%
е 3
 
4.1%
Other values (17) 26
35.6%
Common
ValueCountFrequency (%)
90318
98.0%
- 1157
 
1.3%
' 376
 
0.4%
. 283
 
0.3%
8
 
< 0.1%
( 6
 
< 0.1%
) 6
 
< 0.1%
, 3
 
< 0.1%
1 2
 
< 0.1%
` 2
 
< 0.1%
Other values (12) 12
 
< 0.1%
Greek
ValueCountFrequency (%)
Ι 3
17.6%
Γ 3
17.6%
Σ 2
11.8%
Ρ 1
 
5.9%
Η 1
 
5.9%
Δ 1
 
5.9%
Λ 1
 
5.9%
Ϊ 1
 
5.9%
Α 1
 
5.9%
Ο 1
 
5.9%
Other values (2) 2
11.8%
Hangul
ValueCountFrequency (%)
2
15.4%
2
15.4%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1152390
99.8%
None 1835
 
0.2%
Cyrillic 73
 
< 0.1%
Hangul 13
 
< 0.1%
Punctuation 11
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 109593
 
9.5%
e 106955
 
9.3%
90318
 
7.8%
n 83040
 
7.2%
r 77596
 
6.7%
i 74313
 
6.4%
o 64638
 
5.6%
l 56348
 
4.9%
s 46419
 
4.0%
t 41640
 
3.6%
Other values (58) 401530
34.8%
None
ValueCountFrequency (%)
é 390
21.3%
á 177
 
9.6%
ö 142
 
7.7%
í 138
 
7.5%
ó 135
 
7.4%
ø 99
 
5.4%
ä 97
 
5.3%
ñ 92
 
5.0%
ã 53
 
2.9%
ü 51
 
2.8%
Other values (74) 461
25.1%
Cyrillic
ValueCountFrequency (%)
и 8
 
11.0%
о 7
 
9.6%
н 5
 
6.8%
т 5
 
6.8%
р 4
 
5.5%
в 4
 
5.5%
с 4
 
5.5%
а 4
 
5.5%
й 3
 
4.1%
е 3
 
4.1%
Other values (17) 26
35.6%
Punctuation
ValueCountFrequency (%)
8
72.7%
1
 
9.1%
1
 
9.1%
1
 
9.1%
Hangul
ValueCountFrequency (%)
2
15.4%
2
15.4%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%

Affiliate
Text

MISSING 

Distinct12926
Distinct (%)21.5%
Missing25090
Missing (%)29.4%
Memory size666.8 KiB
2024-02-16T22:19:40.573621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length42
Median length36
Mean length17.212878
Min length10

Characters and Unicode

Total characters1036921
Distinct characters121
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3730 ?
Unique (%)6.2%

Sample

1st rowCrossFit 10 Star
2nd rowCape CrossFit
3rd rowCrossFit Greater Heights
4th rowHigher Life CrossFit
5th rowCrossFit Impi
ValueCountFrequency (%)
crossfit 60239
42.2%
city 981
 
0.7%
the 511
 
0.4%
north 491
 
0.3%
south 481
 
0.3%
valley 474
 
0.3%
iron 447
 
0.3%
west 398
 
0.3%
east 355
 
0.2%
park 336
 
0.2%
Other values (11713) 78000
54.7%
2024-02-16T22:19:41.110678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 138236
13.3%
o 91310
 
8.8%
r 90204
 
8.7%
i 88708
 
8.6%
t 86412
 
8.3%
82477
 
8.0%
C 68727
 
6.6%
F 64858
 
6.3%
e 44687
 
4.3%
a 37854
 
3.7%
Other values (111) 243448
23.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 728230
70.2%
Uppercase Letter 210734
 
20.3%
Space Separator 82481
 
8.0%
Decimal Number 13880
 
1.3%
Other Punctuation 931
 
0.1%
Dash Punctuation 423
 
< 0.1%
Close Punctuation 117
 
< 0.1%
Open Punctuation 117
 
< 0.1%
Format 5
 
< 0.1%
Final Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 138236
19.0%
o 91310
12.5%
r 90204
12.4%
i 88708
12.2%
t 86412
11.9%
e 44687
 
6.1%
a 37854
 
5.2%
n 28621
 
3.9%
l 22985
 
3.2%
u 13324
 
1.8%
Other values (52) 85889
11.8%
Uppercase Letter
ValueCountFrequency (%)
C 68727
32.6%
F 64858
30.8%
S 8480
 
4.0%
B 6418
 
3.0%
T 5209
 
2.5%
A 5136
 
2.4%
R 5086
 
2.4%
M 4797
 
2.3%
P 4591
 
2.2%
L 4351
 
2.1%
Other values (26) 33081
15.7%
Decimal Number
ValueCountFrequency (%)
1 2328
16.8%
0 2151
15.5%
2 1560
11.2%
5 1329
9.6%
3 1266
9.1%
4 1246
9.0%
7 1104
8.0%
8 1032
7.4%
6 984
7.1%
9 880
 
6.3%
Other Punctuation
ValueCountFrequency (%)
. 461
49.5%
' 417
44.8%
& 46
 
4.9%
: 6
 
0.6%
/ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
82477
> 99.9%
  4
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 423
100.0%
Close Punctuation
ValueCountFrequency (%)
) 117
100.0%
Open Punctuation
ValueCountFrequency (%)
( 117
100.0%
Format
ValueCountFrequency (%)
5
100.0%
Final Punctuation
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 938964
90.6%
Common 97957
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 138236
14.7%
o 91310
9.7%
r 90204
9.6%
i 88708
9.4%
t 86412
9.2%
C 68727
 
7.3%
F 64858
 
6.9%
e 44687
 
4.8%
a 37854
 
4.0%
n 28621
 
3.0%
Other values (88) 199347
21.2%
Common
ValueCountFrequency (%)
82477
84.2%
1 2328
 
2.4%
0 2151
 
2.2%
2 1560
 
1.6%
5 1329
 
1.4%
3 1266
 
1.3%
4 1246
 
1.3%
7 1104
 
1.1%
8 1032
 
1.1%
6 984
 
1.0%
Other values (13) 2480
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1036143
99.9%
None 771
 
0.1%
Punctuation 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 138236
13.3%
o 91310
 
8.8%
r 90204
 
8.7%
i 88708
 
8.6%
t 86412
 
8.3%
82477
 
8.0%
C 68727
 
6.6%
F 64858
 
6.3%
e 44687
 
4.3%
a 37854
 
3.7%
Other values (62) 242670
23.4%
None
ValueCountFrequency (%)
ä 123
16.0%
ö 103
13.4%
í 84
10.9%
é 54
 
7.0%
ü 50
 
6.5%
ø 46
 
6.0%
ã 40
 
5.2%
è 34
 
4.4%
á 33
 
4.3%
ç 25
 
3.2%
Other values (37) 179
23.2%
Punctuation
ValueCountFrequency (%)
5
71.4%
2
 
28.6%

Country
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing85331
Missing (%)100.0%
Memory size666.8 KiB

Region
Text

MISSING 

Distinct139
Distinct (%)0.2%
Missing2474
Missing (%)2.9%
Memory size666.8 KiB
2024-02-16T22:19:41.312174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length33
Median length31
Mean length10.761119
Min length4

Characters and Unicode

Total characters891634
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowAfrica
2nd rowAfrica
3rd rowAfrica
4th rowAfrica
5th rowAfrica
ValueCountFrequency (%)
worldwide 25791
19.7%
north 21356
16.3%
america 19742
15.1%
east 8392
 
6.4%
europe 7873
 
6.0%
united 6685
 
5.1%
states 6200
 
4.7%
south 5976
 
4.6%
west 5354
 
4.1%
central 5103
 
3.9%
Other values (139) 18372
14.0%
2024-02-16T22:19:41.627604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 87272
 
9.8%
e 81878
 
9.2%
t 73453
 
8.2%
i 66117
 
7.4%
o 65593
 
7.4%
d 63272
 
7.1%
a 62571
 
7.0%
w 52014
 
5.8%
47987
 
5.4%
l 36629
 
4.1%
Other values (42) 254848
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 738605
82.8%
Uppercase Letter 104962
 
11.8%
Space Separator 47987
 
5.4%
Other Punctuation 80
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 87272
11.8%
e 81878
11.1%
t 73453
9.9%
i 66117
9.0%
o 65593
8.9%
d 63272
8.6%
a 62571
8.5%
w 52014
7.0%
l 36629
 
5.0%
h 28800
 
3.9%
Other values (16) 121006
16.4%
Uppercase Letter
ValueCountFrequency (%)
A 24812
23.6%
N 22209
21.2%
E 16323
15.6%
S 13279
12.7%
C 9221
 
8.8%
U 6703
 
6.4%
W 5354
 
5.1%
M 1796
 
1.7%
O 1552
 
1.5%
L 1105
 
1.1%
Other values (13) 2608
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 79
98.8%
' 1
 
1.2%
Space Separator
ValueCountFrequency (%)
47987
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 843567
94.6%
Common 48067
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 87272
 
10.3%
e 81878
 
9.7%
t 73453
 
8.7%
i 66117
 
7.8%
o 65593
 
7.8%
d 63272
 
7.5%
a 62571
 
7.4%
w 52014
 
6.2%
l 36629
 
4.3%
h 28800
 
3.4%
Other values (39) 225968
26.8%
Common
ValueCountFrequency (%)
47987
99.8%
, 79
 
0.2%
' 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 891633
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 87272
 
9.8%
e 81878
 
9.2%
t 73453
 
8.2%
i 66117
 
7.4%
o 65593
 
7.4%
d 63272
 
7.1%
a 62571
 
7.0%
w 52014
 
5.8%
47987
 
5.4%
l 36629
 
4.1%
Other values (41) 254847
28.6%
None
ValueCountFrequency (%)
ô 1
100.0%

Division
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct33
Distinct (%)< 0.1%
Missing2474
Missing (%)2.9%
Memory size666.8 KiB
Men
41537 
Women
19322 
Men (35-39)
4743 
Men (40-44)
 
3590
Men (45-49)
 
2369
Other values (28)
11296 

Length

Max length21
Median length3
Mean length5.7476616
Min length3

Characters and Unicode

Total characters476234
Distinct characters40
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBoys (14-15)
2nd rowBoys (16-17)
3rd rowMen
4th rowMen
5th rowMen

Common Values

ValueCountFrequency (%)
Men 41537
48.7%
Women 19322
22.6%
Men (35-39) 4743
 
5.6%
Men (40-44) 3590
 
4.2%
Men (45-49) 2369
 
2.8%
Women (35-39) 2181
 
2.6%
Women (40-44) 1686
 
2.0%
Men (55-59) 1665
 
2.0%
Men (50-54) 1432
 
1.7%
Women (45-49) 996
 
1.2%
Other values (23) 3336
 
3.9%
(Missing) 2474
 
2.9%

Length

2024-02-16T22:19:41.780871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
men 56640
54.0%
women 26128
24.9%
35-39 6924
 
6.6%
40-44 5276
 
5.0%
45-49 3365
 
3.2%
55-59 2402
 
2.3%
50-54 2053
 
2.0%
60-64 913
 
0.9%
65 547
 
0.5%
60 299
 
0.3%
Other values (15) 409
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 82981
17.4%
n 82773
17.4%
M 56672
11.9%
m 26240
 
5.5%
o 26238
 
5.5%
W 26128
 
5.5%
4 25562
 
5.4%
5 22186
 
4.7%
22099
 
4.6%
) 21876
 
4.6%
Other values (30) 83479
17.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 219591
46.1%
Decimal Number 85780
 
18.0%
Uppercase Letter 83104
 
17.5%
Space Separator 22099
 
4.6%
Close Punctuation 21876
 
4.6%
Open Punctuation 21876
 
4.6%
Dash Punctuation 21054
 
4.4%
Math Symbol 846
 
0.2%
Other Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 82981
37.8%
n 82773
37.7%
m 26240
 
11.9%
o 26238
 
11.9%
t 279
 
0.1%
r 237
 
0.1%
i 197
 
0.1%
y 166
 
0.1%
x 112
 
0.1%
s 94
 
< 0.1%
Other values (7) 274
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
M 56672
68.2%
W 26128
31.4%
E 112
 
0.1%
B 54
 
0.1%
U 40
 
< 0.1%
L 40
 
< 0.1%
G 35
 
< 0.1%
S 18
 
< 0.1%
V 5
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
4 25562
29.8%
5 22186
25.9%
3 13848
16.1%
9 12691
14.8%
0 8541
 
10.0%
6 2723
 
3.2%
1 178
 
0.2%
7 51
 
0.1%
Space Separator
ValueCountFrequency (%)
22099
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21876
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21876
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21054
100.0%
Math Symbol
ValueCountFrequency (%)
+ 846
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 302695
63.6%
Common 173539
36.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 82981
27.4%
n 82773
27.3%
M 56672
18.7%
m 26240
 
8.7%
o 26238
 
8.7%
W 26128
 
8.6%
t 279
 
0.1%
r 237
 
0.1%
i 197
 
0.1%
y 166
 
0.1%
Other values (16) 784
 
0.3%
Common
ValueCountFrequency (%)
4 25562
14.7%
5 22186
12.8%
22099
12.7%
) 21876
12.6%
( 21876
12.6%
- 21054
12.1%
3 13848
8.0%
9 12691
7.3%
0 8541
 
4.9%
6 2723
 
1.6%
Other values (4) 1083
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 476234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 82981
17.4%
n 82773
17.4%
M 56672
11.9%
m 26240
 
5.5%
o 26238
 
5.5%
W 26128
 
5.5%
4 25562
 
5.4%
5 22186
 
4.7%
22099
 
4.6%
) 21876
 
4.6%
Other values (30) 83479
17.5%

Rank
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14425
Distinct (%)51.4%
Missing57254
Missing (%)67.1%
Infinite0
Infinite (%)0.0%
Mean10411.523
Minimum0
Maximum157102
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size666.8 KiB
2024-02-16T22:19:41.904865image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile121
Q1970
median3781
Q311060
95-th percentile47486.4
Maximum157102
Range157102
Interquartile range (IQR)10090

Descriptive statistics

Standard deviation17896.965
Coefficient of variation (CV)1.7189574
Kurtosis13.936827
Mean10411.523
Median Absolute Deviation (MAD)3302
Skewness3.3647104
Sum2.9232434 × 108
Variance3.2030136 × 108
MonotonicityNot monotonic
2024-02-16T22:19:42.039352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1653 39
 
< 0.1%
1355 33
 
< 0.1%
2132 23
 
< 0.1%
15 21
 
< 0.1%
913 19
 
< 0.1%
12 19
 
< 0.1%
9 18
 
< 0.1%
2306 18
 
< 0.1%
1 18
 
< 0.1%
182 17
 
< 0.1%
Other values (14415) 27852
32.6%
(Missing) 57254
67.1%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 18
< 0.1%
2 16
< 0.1%
3 15
< 0.1%
4 17
< 0.1%
5 11
< 0.1%
6 14
< 0.1%
7 13
< 0.1%
8 12
< 0.1%
9 18
< 0.1%
ValueCountFrequency (%)
157102 1
< 0.1%
155374 1
< 0.1%
153679 1
< 0.1%
153121 1
< 0.1%
152619 1
< 0.1%
150939 1
< 0.1%
149948 1
< 0.1%
149633 1
< 0.1%
149448 1
< 0.1%
149414 1
< 0.1%

Games_Level
Categorical

IMBALANCE  MISSING 

Distinct8
Distinct (%)< 0.1%
Missing57254
Missing (%)67.1%
Memory size666.8 KiB
worldwide
25791 
North America East
 
840
North America West
 
598
Europe
 
491
Oceania
 
164
Other values (3)
 
193

Length

Max length18
Median length9
Mean length9.392314
Min length4

Characters and Unicode

Total characters263708
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfrica
2nd rowAfrica
3rd rowAfrica
4th rowAfrica
5th rowAfrica

Common Values

ValueCountFrequency (%)
worldwide 25791
30.2%
North America East 840
 
1.0%
North America West 598
 
0.7%
Europe 491
 
0.6%
Oceania 164
 
0.2%
South America 83
 
0.1%
Asia 64
 
0.1%
Africa 46
 
0.1%
(Missing) 57254
67.1%

Length

2024-02-16T22:19:42.172001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T22:19:42.288836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
worldwide 25791
83.1%
america 1521
 
4.9%
north 1438
 
4.6%
east 840
 
2.7%
west 598
 
1.9%
europe 491
 
1.6%
oceania 164
 
0.5%
south 83
 
0.3%
asia 64
 
0.2%
africa 46
 
0.1%

Most occurring characters

ValueCountFrequency (%)
w 51582
19.6%
d 51582
19.6%
r 29287
11.1%
e 28565
10.8%
o 27803
10.5%
i 27586
10.5%
l 25791
9.8%
t 2959
 
1.1%
2959
 
1.1%
a 2799
 
1.1%
Other values (14) 12795
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 255504
96.9%
Uppercase Letter 5245
 
2.0%
Space Separator 2959
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 51582
20.2%
d 51582
20.2%
r 29287
11.5%
e 28565
11.2%
o 27803
10.9%
i 27586
10.8%
l 25791
10.1%
t 2959
 
1.2%
a 2799
 
1.1%
c 1731
 
0.7%
Other values (7) 5819
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
A 1631
31.1%
N 1438
27.4%
E 1331
25.4%
W 598
 
11.4%
O 164
 
3.1%
S 83
 
1.6%
Space Separator
ValueCountFrequency (%)
2959
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 260749
98.9%
Common 2959
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 51582
19.8%
d 51582
19.8%
r 29287
11.2%
e 28565
11.0%
o 27803
10.7%
i 27586
10.6%
l 25791
9.9%
t 2959
 
1.1%
a 2799
 
1.1%
c 1731
 
0.7%
Other values (13) 11064
 
4.2%
Common
ValueCountFrequency (%)
2959
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 263708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 51582
19.6%
d 51582
19.6%
r 29287
11.1%
e 28565
10.8%
o 27803
10.5%
i 27586
10.5%
l 25791
9.8%
t 2959
 
1.1%
2959
 
1.1%
a 2799
 
1.1%
Other values (14) 12795
 
4.9%

Year
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing57254
Missing (%)67.1%
Memory size666.8 KiB
2023.0
28077 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters168462
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023.0
2nd row2023.0
3rd row2023.0
4th row2023.0
5th row2023.0

Common Values

ValueCountFrequency (%)
2023.0 28077
32.9%
(Missing) 57254
67.1%

Length

2024-02-16T22:19:42.401547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T22:19:42.482748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2023.0 28077
100.0%

Most occurring characters

ValueCountFrequency (%)
2 56154
33.3%
0 56154
33.3%
3 28077
16.7%
. 28077
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140385
83.3%
Other Punctuation 28077
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 56154
40.0%
0 56154
40.0%
3 28077
20.0%
Other Punctuation
ValueCountFrequency (%)
. 28077
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 168462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 56154
33.3%
0 56154
33.3%
3 28077
16.7%
. 28077
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 168462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 56154
33.3%
0 56154
33.3%
3 28077
16.7%
. 28077
16.7%

Qualifier
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing57254
Missing (%)67.1%
Memory size666.8 KiB
open
21917 
quarterfinals
5752 
semifinals
 
284
games
 
124

Length

Max length13
Median length4
Mean length5.9088934
Min length4

Characters and Unicode

Total characters165904
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquarterfinals
2nd rowquarterfinals
3rd rowquarterfinals
4th rowquarterfinals
5th rowquarterfinals

Common Values

ValueCountFrequency (%)
open 21917
 
25.7%
quarterfinals 5752
 
6.7%
semifinals 284
 
0.3%
games 124
 
0.1%
(Missing) 57254
67.1%

Length

2024-02-16T22:19:42.567241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T22:19:42.654619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
open 21917
78.1%
quarterfinals 5752
 
20.5%
semifinals 284
 
1.0%
games 124
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 28077
16.9%
n 27953
16.8%
o 21917
13.2%
p 21917
13.2%
a 11912
7.2%
r 11504
6.9%
s 6444
 
3.9%
i 6320
 
3.8%
f 6036
 
3.6%
l 6036
 
3.6%
Other values (5) 17788
10.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 165904
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 28077
16.9%
n 27953
16.8%
o 21917
13.2%
p 21917
13.2%
a 11912
7.2%
r 11504
6.9%
s 6444
 
3.9%
i 6320
 
3.8%
f 6036
 
3.6%
l 6036
 
3.6%
Other values (5) 17788
10.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 165904
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 28077
16.9%
n 27953
16.8%
o 21917
13.2%
p 21917
13.2%
a 11912
7.2%
r 11504
6.9%
s 6444
 
3.9%
i 6320
 
3.8%
f 6036
 
3.6%
l 6036
 
3.6%
Other values (5) 17788
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 28077
16.9%
n 27953
16.8%
o 21917
13.2%
p 21917
13.2%
a 11912
7.2%
r 11504
6.9%
s 6444
 
3.9%
i 6320
 
3.8%
f 6036
 
3.6%
l 6036
 
3.6%
Other values (5) 17788
10.7%

Back Squat (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct718
Distinct (%)0.9%
Missing7460
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean302.70883
Minimum3
Maximum1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size666.8 KiB
2024-02-16T22:19:42.770057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile165
Q1235
median305
Q3365
95-th percentile440.924
Maximum1012
Range1009
Interquartile range (IQR)130

Descriptive statistics

Standard deviation86.458188
Coefficient of variation (CV)0.28561502
Kurtosis0.16814343
Mean302.70883
Median Absolute Deviation (MAD)62.4918
Skewness0.1574522
Sum23572239
Variance7475.0183
MonotonicityNot monotonic
2024-02-16T22:19:42.902930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
315 2277
 
2.7%
365 2005
 
2.3%
405 1879
 
2.2%
335 1683
 
2.0%
225 1597
 
1.9%
275 1546
 
1.8%
325 1539
 
1.8%
385 1498
 
1.8%
305 1425
 
1.7%
330.693 1387
 
1.6%
Other values (708) 61035
71.5%
(Missing) 7460
 
8.7%
ValueCountFrequency (%)
3 4
< 0.1%
4 4
< 0.1%
4.40924 3
< 0.1%
5 1
 
< 0.1%
6.61386 1
 
< 0.1%
7 3
< 0.1%
8.81848 1
 
< 0.1%
10 5
< 0.1%
11.0231 3
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
1012 1
< 0.1%
1000 1
< 0.1%
999 1
< 0.1%
930 1
< 0.1%
925 1
< 0.1%
903 1
< 0.1%
900 2
< 0.1%
892.8711 1
< 0.1%
890 2
< 0.1%
875 1
< 0.1%

Clean and Jerk (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct540
Distinct (%)0.7%
Missing10332
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean214.15083
Minimum3
Maximum587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size666.8 KiB
2024-02-16T22:19:43.027500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile115
Q1165.3465
median215
Q3255.73592
95-th percentile315
Maximum587
Range584
Interquartile range (IQR)90.38942

Descriptive statistics

Standard deviation61.187089
Coefficient of variation (CV)0.2857196
Kurtosis-0.35089763
Mean214.15083
Median Absolute Deviation (MAD)45
Skewness0.072199641
Sum16061098
Variance3743.8598
MonotonicityNot monotonic
2024-02-16T22:19:43.156015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225 3068
 
3.6%
245 2465
 
2.9%
185 2268
 
2.7%
205 2221
 
2.6%
235 2119
 
2.5%
275 2115
 
2.5%
265 2030
 
2.4%
255 1966
 
2.3%
215 1864
 
2.2%
165 1722
 
2.0%
Other values (530) 53161
62.3%
(Missing) 10332
 
12.1%
ValueCountFrequency (%)
3 1
 
< 0.1%
4.40924 1
 
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
10 6
< 0.1%
11.0231 2
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
17 1
 
< 0.1%
ValueCountFrequency (%)
587 1
< 0.1%
586.42892 1
< 0.1%
584.2243 1
< 0.1%
555.56424 1
< 0.1%
546.74576 1
< 0.1%
544.54114 1
< 0.1%
542.33652 1
< 0.1%
540.1319 1
< 0.1%
518.0857 1
< 0.1%
509.26722 1
< 0.1%

Deadlift (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct757
Distinct (%)1.0%
Missing5954
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean368.59866
Minimum3
Maximum1135.3793
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size666.8 KiB
2024-02-16T22:19:43.323772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile215
Q1297.6237
median374.7854
Q3440.924
95-th percentile515
Maximum1135.3793
Range1132.3793
Interquartile range (IQR)143.3003

Descriptive statistics

Standard deviation96.684367
Coefficient of variation (CV)0.26230255
Kurtosis-0.14509493
Mean368.59866
Median Absolute Deviation (MAD)70.2146
Skewness-0.019742057
Sum29258256
Variance9347.8667
MonotonicityNot monotonic
2024-02-16T22:19:43.454916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
405 3177
 
3.7%
425 2012
 
2.4%
365 1816
 
2.1%
455 1727
 
2.0%
315 1716
 
2.0%
440.924 1675
 
2.0%
415 1576
 
1.8%
435 1499
 
1.8%
385 1447
 
1.7%
396.8316 1415
 
1.7%
Other values (747) 61317
71.9%
(Missing) 5954
 
7.0%
ValueCountFrequency (%)
3 5
< 0.1%
4.40924 4
< 0.1%
5 3
< 0.1%
6 1
 
< 0.1%
6.61386 1
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
8.81848 1
 
< 0.1%
10 3
< 0.1%
11.0231 4
< 0.1%
ValueCountFrequency (%)
1135.3793 1
< 0.1%
1106.71924 1
< 0.1%
1102.31 1
< 0.1%
1100 1
< 0.1%
1000 1
< 0.1%
999 1
< 0.1%
986 1
< 0.1%
981.0559 1
< 0.1%
955 1
< 0.1%
901 1
< 0.1%

Snatch (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct458
Distinct (%)0.6%
Missing13051
Missing (%)15.3%
Infinite0
Infinite (%)0.0%
Mean164.93821
Minimum1
Maximum489.42564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size666.8 KiB
2024-02-16T22:19:43.580340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile85
Q1125
median165
Q3200.62042
95-th percentile250
Maximum489.42564
Range488.42564
Interquartile range (IQR)75.62042

Descriptive statistics

Standard deviation51.210787
Coefficient of variation (CV)0.31048469
Kurtosis-0.25774085
Mean164.93821
Median Absolute Deviation (MAD)37.82504
Skewness0.14910566
Sum11921733
Variance2622.5447
MonotonicityNot monotonic
2024-02-16T22:19:43.724765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185 2968
 
3.5%
135 2660
 
3.1%
165 2516
 
2.9%
155 2494
 
2.9%
175 2257
 
2.6%
205 2118
 
2.5%
145 1923
 
2.3%
225 1895
 
2.2%
115 1866
 
2.2%
125 1661
 
1.9%
Other values (448) 49922
58.5%
(Missing) 13051
 
15.3%
ValueCountFrequency (%)
1 32
< 0.1%
2 4
 
< 0.1%
2.20462 25
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
4.40924 2
 
< 0.1%
5 4
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8.81848 1
 
< 0.1%
ValueCountFrequency (%)
489.42564 2
< 0.1%
476.19792 1
< 0.1%
462.9702 1
< 0.1%
455 1
< 0.1%
440.924 2
< 0.1%
421.08242 1
< 0.1%
415 1
< 0.1%
412.26394 1
< 0.1%
412 1
< 0.1%
407.8547 2
< 0.1%

Fight Gone Bad
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct493
Distinct (%)2.3%
Missing63744
Missing (%)74.7%
Infinite0
Infinite (%)0.0%
Mean310.99018
Minimum15
Maximum978
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size666.8 KiB
2024-02-16T22:19:43.856178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile211
Q1269
median309
Q3350
95-th percentile419
Maximum978
Range963
Interquartile range (IQR)81

Descriptive statistics

Standard deviation66.549543
Coefficient of variation (CV)0.21399243
Kurtosis4.5946989
Mean310.99018
Median Absolute Deviation (MAD)40
Skewness0.57497997
Sum6713345
Variance4428.8417
MonotonicityNot monotonic
2024-02-16T22:19:43.986998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 236
 
0.3%
301 215
 
0.3%
315 214
 
0.3%
308 194
 
0.2%
302 185
 
0.2%
306 177
 
0.2%
307 175
 
0.2%
325 175
 
0.2%
305 174
 
0.2%
312 172
 
0.2%
Other values (483) 19670
 
23.1%
(Missing) 63744
74.7%
ValueCountFrequency (%)
15 6
< 0.1%
17 2
 
< 0.1%
20 4
< 0.1%
21 1
 
< 0.1%
25 4
< 0.1%
28 2
 
< 0.1%
30 3
< 0.1%
31 1
 
< 0.1%
32 1
 
< 0.1%
33 1
 
< 0.1%
ValueCountFrequency (%)
978 2
< 0.1%
959 1
< 0.1%
923 1
< 0.1%
892 1
< 0.1%
890 1
< 0.1%
879 1
< 0.1%
876 1
< 0.1%
832 2
< 0.1%
812 1
< 0.1%
789 1
< 0.1%

Max Pull-ups
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct133
Distinct (%)0.4%
Missing49677
Missing (%)58.2%
Infinite0
Infinite (%)0.0%
Mean31.997981
Minimum1
Maximum627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size666.8 KiB
2024-02-16T22:19:44.110025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q120
median30
Q342
95-th percentile60
Maximum627
Range626
Interquartile range (IQR)22

Descriptive statistics

Standard deviation18.585165
Coefficient of variation (CV)0.58082305
Kurtosis119.31764
Mean31.997981
Median Absolute Deviation (MAD)11
Skewness5.0532093
Sum1140856
Variance345.40835
MonotonicityNot monotonic
2024-02-16T22:19:44.238717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 2612
 
3.1%
50 1872
 
2.2%
40 1822
 
2.1%
25 1809
 
2.1%
20 1701
 
2.0%
35 1372
 
1.6%
15 1241
 
1.5%
21 1126
 
1.3%
10 1122
 
1.3%
45 880
 
1.0%
Other values (123) 20097
23.6%
(Missing) 49677
58.2%
ValueCountFrequency (%)
1 287
 
0.3%
2 198
 
0.2%
3 310
 
0.4%
4 168
 
0.2%
5 477
0.6%
6 257
 
0.3%
7 282
 
0.3%
8 331
 
0.4%
9 148
 
0.2%
10 1122
1.3%
ValueCountFrequency (%)
627 1
 
< 0.1%
614 1
 
< 0.1%
553 1
 
< 0.1%
500 2
< 0.1%
438 1
 
< 0.1%
420 1
 
< 0.1%
400 1
 
< 0.1%
304 1
 
< 0.1%
300 3
< 0.1%
235 1
 
< 0.1%

Chad1000x (s)
Real number (ℝ)

MISSING 

Distinct458
Distinct (%)84.8%
Missing84791
Missing (%)99.4%
Infinite0
Infinite (%)0.0%
Mean4057.6093
Minimum1695
Maximum8956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size666.8 KiB
2024-02-16T22:19:44.364703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1695
5-th percentile2914.3
Q13430.75
median3896.5
Q34516.75
95-th percentile5726.15
Maximum8956
Range7261
Interquartile range (IQR)1086

Descriptive statistics

Standard deviation937.69773
Coefficient of variation (CV)0.23109611
Kurtosis4.490248
Mean4057.6093
Median Absolute Deviation (MAD)536.5
Skewness1.5157557
Sum2191109
Variance879277.04
MonotonicityNot monotonic
2024-02-16T22:19:44.493457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3900 6
 
< 0.1%
4020 5
 
< 0.1%
3600 5
 
< 0.1%
4560 5
 
< 0.1%
4320 4
 
< 0.1%
3360 3
 
< 0.1%
4800 3
 
< 0.1%
3840 3
 
< 0.1%
3540 3
 
< 0.1%
3539 3
 
< 0.1%
Other values (448) 500
 
0.6%
(Missing) 84791
99.4%
ValueCountFrequency (%)
1695 1
< 0.1%
2068 1
< 0.1%
2280 1
< 0.1%
2382 1
< 0.1%
2483 1
< 0.1%
2557 1
< 0.1%
2573 1
< 0.1%
2604 1
< 0.1%
2627 1
< 0.1%
2635 1
< 0.1%
ValueCountFrequency (%)
8956 1
< 0.1%
8760 1
< 0.1%
8664 1
< 0.1%
8461 1
< 0.1%
7968 1
< 0.1%
7200 1
< 0.1%
7140 1
< 0.1%
6907 1
< 0.1%
6831 1
< 0.1%
6365 1
< 0.1%

L1 Benchmark (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115
Distinct (%)77.2%
Missing85182
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean357.51678
Minimum10
Maximum1800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size666.8 KiB
2024-02-16T22:19:44.621321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile174.8
Q1245
median321
Q3395
95-th percentile618.6
Maximum1800
Range1790
Interquartile range (IQR)150

Descriptive statistics

Standard deviation215.49044
Coefficient of variation (CV)0.60274217
Kurtosis21.679417
Mean357.51678
Median Absolute Deviation (MAD)74
Skewness3.9244697
Sum53270
Variance46436.13
MonotonicityNot monotonic
2024-02-16T22:19:44.749552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360 5
 
< 0.1%
395 4
 
< 0.1%
300 4
 
< 0.1%
243 3
 
< 0.1%
312 3
 
< 0.1%
267 2
 
< 0.1%
600 2
 
< 0.1%
244 2
 
< 0.1%
268 2
 
< 0.1%
403 2
 
< 0.1%
Other values (105) 120
 
0.1%
(Missing) 85182
99.8%
ValueCountFrequency (%)
10 1
< 0.1%
62 1
< 0.1%
65 1
< 0.1%
119 1
< 0.1%
135 1
< 0.1%
162 1
< 0.1%
163 1
< 0.1%
172 1
< 0.1%
179 1
< 0.1%
180 2
< 0.1%
ValueCountFrequency (%)
1800 1
< 0.1%
1625 1
< 0.1%
1200 1
< 0.1%
850 1
< 0.1%
681 1
< 0.1%
677 1
< 0.1%
656 1
< 0.1%
625 1
< 0.1%
609 1
< 0.1%
600 2
< 0.1%

Filthy 50 (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1811
Distinct (%)12.2%
Missing70433
Missing (%)82.5%
Infinite0
Infinite (%)0.0%
Mean1567.2269
Minimum310
Maximum3599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size666.8 KiB
2024-02-16T22:19:44.871686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum310
5-th percentile1063.85
Q11298
median1519
Q31774
95-th percentile2254
Maximum3599
Range3289
Interquartile range (IQR)476

Descriptive statistics

Standard deviation377.34844
Coefficient of variation (CV)0.24077461
Kurtosis1.6523825
Mean1567.2269
Median Absolute Deviation (MAD)236
Skewness0.89238922
Sum23348546
Variance142391.84
MonotonicityNot monotonic
2024-02-16T22:19:44.998067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 97
 
0.1%
1500 83
 
0.1%
1200 68
 
0.1%
1320 59
 
0.1%
1680 57
 
0.1%
1740 53
 
0.1%
1440 53
 
0.1%
1620 49
 
0.1%
1350 48
 
0.1%
1260 47
 
0.1%
Other values (1801) 14284
 
16.7%
(Missing) 70433
82.5%
ValueCountFrequency (%)
310 1
< 0.1%
315 1
< 0.1%
325 1
< 0.1%
330 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
355 1
< 0.1%
368 1
< 0.1%
435 1
< 0.1%
480 2
< 0.1%
ValueCountFrequency (%)
3599 2
< 0.1%
3591 1
 
< 0.1%
3537 1
 
< 0.1%
3510 1
 
< 0.1%
3504 1
 
< 0.1%
3483 1
 
< 0.1%
3404 1
 
< 0.1%
3395 1
 
< 0.1%
3360 3
< 0.1%
3355 1
 
< 0.1%

Fran (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct856
Distinct (%)1.9%
Missing39621
Missing (%)46.4%
Infinite0
Infinite (%)0.0%
Mean284.89453
Minimum72
Maximum3552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size666.8 KiB
2024-02-16T22:19:45.124915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile140
Q1186
median254
Q3346
95-th percentile528
Maximum3552
Range3480
Interquartile range (IQR)160

Descriptive statistics

Standard deviation144.11868
Coefficient of variation (CV)0.50586678
Kurtosis67.466591
Mean284.89453
Median Absolute Deviation (MAD)76
Skewness4.7074386
Sum13022529
Variance20770.193
MonotonicityNot monotonic
2024-02-16T22:19:45.255896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210 384
 
0.5%
178 377
 
0.4%
270 321
 
0.4%
240 315
 
0.4%
150 306
 
0.4%
180 287
 
0.3%
177 275
 
0.3%
179 272
 
0.3%
195 272
 
0.3%
225 270
 
0.3%
Other values (846) 42631
50.0%
(Missing) 39621
46.4%
ValueCountFrequency (%)
72 3
< 0.1%
75 2
< 0.1%
76 2
< 0.1%
78 1
 
< 0.1%
79 1
 
< 0.1%
80 3
< 0.1%
85 1
 
< 0.1%
86 1
 
< 0.1%
89 1
 
< 0.1%
90 1
 
< 0.1%
ValueCountFrequency (%)
3552 1
< 0.1%
3540 1
< 0.1%
3480 2
< 0.1%
3355 1
< 0.1%
3348 1
< 0.1%
3258 1
< 0.1%
3165 1
< 0.1%
3007 1
< 0.1%
2854 1
< 0.1%
2730 1
< 0.1%

Grace (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct738
Distinct (%)2.0%
Missing48249
Missing (%)56.5%
Infinite0
Infinite (%)0.0%
Mean199.70781
Minimum14
Maximum3480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size666.8 KiB
2024-02-16T22:19:45.383960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile99
Q1135
median175
Q3232
95-th percentile372
Maximum3480
Range3466
Interquartile range (IQR)97

Descriptive statistics

Standard deviation113.19433
Coefficient of variation (CV)0.5667997
Kurtosis115.67113
Mean199.70781
Median Absolute Deviation (MAD)45
Skewness6.6977437
Sum7405565
Variance12812.956
MonotonicityNot monotonic
2024-02-16T22:19:45.516929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118 438
 
0.5%
150 407
 
0.5%
178 397
 
0.5%
180 371
 
0.4%
165 334
 
0.4%
210 328
 
0.4%
160 324
 
0.4%
140 309
 
0.4%
135 307
 
0.4%
120 295
 
0.3%
Other values (728) 33572
39.3%
(Missing) 48249
56.5%
ValueCountFrequency (%)
14 1
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
30 1
 
< 0.1%
32 3
< 0.1%
46 1
 
< 0.1%
47 1
 
< 0.1%
49 1
 
< 0.1%
50 2
< 0.1%
52 1
 
< 0.1%
ValueCountFrequency (%)
3480 1
< 0.1%
3362 1
< 0.1%
3355 1
< 0.1%
3000 1
< 0.1%
2820 1
< 0.1%
2745 1
< 0.1%
2723 1
< 0.1%
2574 1
< 0.1%
2279 1
< 0.1%
1920 1
< 0.1%

Helen (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct900
Distinct (%)3.4%
Missing58791
Missing (%)68.9%
Infinite0
Infinite (%)0.0%
Mean619.02253
Minimum300
Maximum3363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size666.8 KiB
2024-02-16T22:19:45.787661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile455
Q1524
median590
Q3684
95-th percentile867
Maximum3363
Range3063
Interquartile range (IQR)160

Descriptive statistics

Standard deviation149.69709
Coefficient of variation (CV)0.24182817
Kurtosis45.051205
Mean619.02253
Median Absolute Deviation (MAD)76
Skewness3.9126719
Sum16428858
Variance22409.218
MonotonicityNot monotonic
2024-02-16T22:19:45.912992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
570 180
 
0.2%
540 171
 
0.2%
510 167
 
0.2%
585 161
 
0.2%
530 148
 
0.2%
600 139
 
0.2%
520 136
 
0.2%
598 135
 
0.2%
478 133
 
0.2%
480 133
 
0.2%
Other values (890) 25037
29.3%
(Missing) 58791
68.9%
ValueCountFrequency (%)
300 4
< 0.1%
301 1
 
< 0.1%
305 2
< 0.1%
306 1
 
< 0.1%
307 1
 
< 0.1%
308 1
 
< 0.1%
309 1
 
< 0.1%
310 1
 
< 0.1%
312 1
 
< 0.1%
315 1
 
< 0.1%
ValueCountFrequency (%)
3363 1
< 0.1%
3355 1
< 0.1%
3271 1
< 0.1%
3252 1
< 0.1%
3202 1
< 0.1%
3198 1
< 0.1%
3130 1
< 0.1%
3124 1
< 0.1%
2956 1
< 0.1%
2715 1
< 0.1%

Run 5k (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1545
Distinct (%)5.1%
Missing54881
Missing (%)64.3%
Infinite0
Infinite (%)0.0%
Mean1440.4465
Minimum761
Maximum3585
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size666.8 KiB
2024-02-16T22:19:46.037425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum761
5-th percentile1110
Q11258
median1389
Q31573
95-th percentile1920
Maximum3585
Range2824
Interquartile range (IQR)315

Descriptive statistics

Standard deviation277.28966
Coefficient of variation (CV)0.19250257
Kurtosis6.0663376
Mean1440.4465
Median Absolute Deviation (MAD)156
Skewness1.7101892
Sum43861597
Variance76889.557
MonotonicityNot monotonic
2024-02-16T22:19:46.160477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 685
 
0.8%
1260 662
 
0.8%
1320 624
 
0.7%
1200 540
 
0.6%
1440 538
 
0.6%
1380 524
 
0.6%
1620 412
 
0.5%
1680 397
 
0.5%
1800 352
 
0.4%
1350 320
 
0.4%
Other values (1535) 25396
29.8%
(Missing) 54881
64.3%
ValueCountFrequency (%)
761 1
< 0.1%
767 1
< 0.1%
768 1
< 0.1%
780 1
< 0.1%
792 1
< 0.1%
808 1
< 0.1%
810 1
< 0.1%
820 1
< 0.1%
828 1
< 0.1%
831 1
< 0.1%
ValueCountFrequency (%)
3585 1
 
< 0.1%
3562 1
 
< 0.1%
3556 1
 
< 0.1%
3544 2
< 0.1%
3540 3
< 0.1%
3538 1
 
< 0.1%
3527 1
 
< 0.1%
3517 1
 
< 0.1%
3515 1
 
< 0.1%
3501 1
 
< 0.1%

Sprint 400m (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct194
Distinct (%)1.0%
Missing66642
Missing (%)78.1%
Infinite0
Infinite (%)0.0%
Mean77.348066
Minimum44
Maximum378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size666.8 KiB
2024-02-16T22:19:46.281598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile54
Q163
median72
Q386
95-th percentile115
Maximum378
Range334
Interquartile range (IQR)23

Descriptive statistics

Standard deviation23.870818
Coefficient of variation (CV)0.30861558
Kurtosis27.617896
Mean77.348066
Median Absolute Deviation (MAD)11
Skewness3.8007526
Sum1445558
Variance569.81597
MonotonicityNot monotonic
2024-02-16T22:19:46.409875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 772
 
0.9%
65 767
 
0.9%
60 754
 
0.9%
75 723
 
0.8%
80 617
 
0.7%
62 550
 
0.6%
90 539
 
0.6%
58 523
 
0.6%
63 507
 
0.6%
66 482
 
0.6%
Other values (184) 12455
 
14.6%
(Missing) 66642
78.1%
ValueCountFrequency (%)
44 4
 
< 0.1%
45 14
 
< 0.1%
46 17
 
< 0.1%
47 28
 
< 0.1%
48 55
 
0.1%
49 83
0.1%
50 103
0.1%
51 107
0.1%
52 205
0.2%
53 141
0.2%
ValueCountFrequency (%)
378 1
< 0.1%
366 1
< 0.1%
365 1
< 0.1%
364 1
< 0.1%
362 1
< 0.1%
349 1
< 0.1%
346 1
< 0.1%
337 1
< 0.1%
331 1
< 0.1%
330 1
< 0.1%

Interactions

2024-02-16T22:19:36.707872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:17.633764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:19.054956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:20.411276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:21.917303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-02-16T22:19:32.366790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:33.737628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:35.217808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:36.536173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:37.957874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:18.958166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:20.320027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:21.694903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:23.165271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:24.499060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:25.807271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:27.148916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:28.550257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:29.747882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:31.080049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:32.454300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:33.825610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:35.302680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-16T22:19:36.619944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-16T22:19:46.515698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Back Squat (lbs)Chad1000x (s)Clean and Jerk (lbs)Deadlift (lbs)DivisionFight Gone BadFilthy 50 (s)Fran (s)Games_LevelGrace (s)Helen (s)L1 Benchmark (s)Max Pull-upsQualifierRankRun 5k (s)Snatch (lbs)Sprint 400m (s)
Back Squat (lbs)1.000-0.0300.9040.9100.2400.494-0.318-0.5670.056-0.522-0.444-0.3710.5180.103-0.069-0.3410.874-0.433
Chad1000x (s)-0.0301.000-0.076-0.0150.147-0.2310.2450.2280.1360.1640.2880.196-0.1560.000-0.0110.261-0.1180.291
Clean and Jerk (lbs)0.904-0.0761.0000.8790.2690.569-0.388-0.6420.090-0.591-0.518-0.4910.6020.140-0.078-0.4020.953-0.503
Deadlift (lbs)0.910-0.0150.8791.0000.2630.494-0.311-0.5230.052-0.500-0.454-0.3500.5140.101-0.040-0.3690.843-0.447
Division0.2400.1470.2690.2631.000-0.2420.1470.2360.1690.1430.2910.132-0.3590.097-0.3190.347-0.6210.367
Fight Gone Bad0.494-0.2310.5690.494-0.2421.000-0.652-0.7010.059-0.634-0.674-0.6050.5730.171-0.275-0.5130.576-0.507
Filthy 50 (s)-0.3180.245-0.388-0.3110.147-0.6521.0000.6430.0430.4810.6540.536-0.5440.1450.2640.497-0.3990.447
Fran (s)-0.5670.228-0.642-0.5230.236-0.7010.6431.0000.0380.6760.7210.656-0.7250.0940.3140.470-0.6510.494
Games_Level0.0560.1360.0900.0520.1690.0590.0430.0381.0000.2080.1610.240-0.1990.3380.3140.132-0.2090.143
Grace (s)-0.5220.164-0.591-0.5000.143-0.6340.4810.6760.2081.0000.5180.573-0.4730.0470.2610.309-0.5920.391
Helen (s)-0.4440.288-0.518-0.4540.291-0.6740.6540.7210.1610.5181.0000.557-0.6620.1270.2340.616-0.5230.590
L1 Benchmark (s)-0.3710.196-0.491-0.3500.132-0.6050.5360.6560.2400.5730.5571.000-0.5100.2200.3330.450-0.4890.425
Max Pull-ups0.518-0.1560.6020.514-0.3590.573-0.544-0.725-0.199-0.473-0.662-0.5101.0000.089-0.244-0.4860.614-0.516
Qualifier0.1030.0000.1400.1010.0970.1710.1450.0940.3380.0470.1270.2200.0891.000-0.595-0.1620.207-0.150
Rank-0.069-0.011-0.078-0.040-0.319-0.2750.2640.3140.3140.2610.2340.333-0.244-0.5951.0000.158-0.0880.101
Run 5k (s)-0.3410.261-0.402-0.3690.347-0.5130.4970.4700.1320.3090.6160.450-0.486-0.1620.1581.000-0.3980.606
Snatch (lbs)0.874-0.1180.9530.843-0.6210.576-0.399-0.651-0.209-0.592-0.523-0.4890.6140.207-0.088-0.3981.000-0.489
Sprint 400m (s)-0.4330.291-0.503-0.4470.367-0.5070.4470.4940.1430.3910.5900.425-0.516-0.1500.1010.606-0.4891.000

Missing values

2024-02-16T22:19:38.124860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-16T22:19:38.558727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-16T22:19:39.366664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AthleteAffiliateCountryRegionDivisionRankGames_LevelYearQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)
0Yagiz DiyarogluNaNNaNAfricaBoys (14-15)NaNNaNNaNNaN187.3927NaN242.50820NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1Jandré ErasmusNaNNaNAfricaBoys (16-17)NaNNaNNaNNaN265.0000174.00000342.00000NaNNaN35.0NaNNaNNaNNaNNaNNaNNaNNaN
2Roan van HeerdenCrossFit 10 StarNaNAfricaMenNaNNaNNaNNaN220.4620176.36960264.55440132.27720291.01.0NaNNaNNaN540.0NaNNaN2400.0120.0
3David SwartsCape CrossFitNaNAfricaMenNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaN
4Colin PinkhamNaNNaNAfricaMenNaNNaNNaNNaN264.5544163.14188365.96692103.61714NaN3.0NaNNaNNaNNaNNaNNaN1731.0NaN
5Aaron WhiteNaNNaNAfricaMenNaNNaNNaNNaN330.6930187.39270429.90090143.30030267.05.0NaNNaNNaNNaN236.0NaN1757.090.0
6Tobi ShowunmiCrossFit Greater HeightsNaNAfricaMenNaNNaNNaNNaN175.0000100.00000150.00000100.00000NaN5.0NaNNaNNaNNaNNaNNaNNaNNaN
7Justin BehrendtNaNNaNAfricaMenNaNNaNNaNNaN352.7392198.41580440.92400132.27720NaN5.0NaNNaNNaNNaNNaNNaNNaNNaN
8Ernst VeldsmanHigher Life CrossFitNaNAfricaMenNaNNaNNaNNaN352.7392264.55440485.01640187.39270NaN7.0NaNNaNNaNNaNNaNNaNNaNNaN
9Christopher WebsterCrossFit ImpiNaNAfricaMenNaNNaNNaNNaNNaN141.09568275.57750NaN262.08.0NaNNaNNaNNaNNaNNaNNaN62.0
AthleteAffiliateCountryRegionDivisionRankGames_LevelYearQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)
85321Thomas CheekNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1200.0NaN
85322Thomas PacherNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1245.0NaN
85323Tim KütemeyerNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN869.052.0
85324Todd IrwinNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1418.0NaN
85325Tony UbertaccioNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN250.0NaNNaNNaNNaN
85326Troels Kjær AaskildeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1196.0NaN
85327Wafik Abed El MalakNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1220.0NaN
85328Will MarshallCrossFit AthleticsNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN187.0NaNNaNNaNNaNNaN252.0NaNNaNNaNNaN
85329Wouter VanloffeltNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1185.064.0
85330Zachary TrolleyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN90.0NaNNaNNaNNaN

Duplicate rows

Most frequently occurring

AthleteAffiliateRegionDivisionRankGames_LevelYearQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)# duplicates
0Aaron McNultyCrossFit FrankfortworldwideMen (45-49)10619.0worldwide2023.0open315.0000195.0000435.0000175.00000NaN10.0NaNNaNNaN474.0NaN831.01680.072.02
1Aaron NewmanCrossFit CatonsvilleworldwideMen (40-44)21776.0worldwide2023.0open235.0000145.0000340.000085.00000186.08.0NaNNaN1665.0480.0277.0773.0NaNNaN2
2Abbie MickeBlack Iron CrossFitUnited StatesWomenNaNNaNNaNNaN195.0000154.3234265.0000132.27720NaNNaNNaNNaNNaNNaNNaNNaN1864.0NaN2
3Abel JosephNaNNorth AmericaMenNaNNaNNaNNaN335.0000225.0000385.0000185.00000207.08.0NaNNaN3065.0813.0262.01051.01896.092.02
4Abigail PaulsCrossFit Strength StationUnited KingdomWomenNaNNaNNaNNaN175.0000125.0000195.0000100.00000NaNNaNNaNNaN3133.0576.0NaN860.01583.0NaN2
5Adam Rivera FigueroaNaNLatin AmericaMenNaNNaNNaNNaN315.0000245.0000355.0000210.00000NaN36.0NaNNaN1594.0195.0171.0NaN1310.051.02
6Adrian Diaz de LeonNaNLatin AmericaMenNaNNaNNaNNaN265.0000185.0000395.0000165.00000NaNNaNNaNNaNNaNNaNNaNNaN1725.065.02
7Agoutin BriceFair Fight CrossFitworldwideMen (35-39)8276.0worldwide2023.0open264.5544220.4620385.8085169.75574NaN43.0NaNNaNNaN239.0NaNNaN1500.0NaN2
8Alan ZedakerNaNworldwideMen (35-39)1342.0worldwide2023.0quarterfinals400.0000300.0000510.0000205.00000NaNNaNNaNNaN1564.0232.0169.0558.01299.0NaN2
9Aldrin PinedaCrossFit Invictus Back BayworldwideMen (35-39)18852.0worldwide2023.0open385.0000235.0000455.0000165.00000NaN3.0NaNNaNNaNNaNNaNNaN1955.0NaN2